Predicting Covid-19 Dynamics: Integrating Statistical Methods for Sirrsd Parameters Estimation in Malaysia, China and India

The COVID-19 pandemic consequences ended up resulting in a high level of complexity with the prediction of upcoming outbreaks for a new scientific study. Mathematical algorithms related to epidemiology are developed in order to accurately estimate the infected cases. In response to the evolving dynamics of COVID-19, this study enhances the traditional Susceptible-Infected-Recovered (SIR) model by incorporating additional compartments, including Dead and Re-susceptibility, resulting in the SIRRSD model. This was done to investigate the dynamic nature of COVID-19 by estimating the parameters and demonstrate the disease spread trajectory in Malaysia, China, and India. By using the Least Squares Method for parameter estimation, the study provides a reliable regression analysis that helps identify critical trends in the spread of COVID-19. The results offer valuable insights for public health officials and policymakers, enabling them to implement timely and effective interventions to mitigate the impact of future outbreaks. These findings underscore the importance of utilizing such advanced algorithms to better prepare for and manage pandemic crises.